def populateFeedGraph(self, shuffle=True, cutPaperOut=False): with tf.name_scope("load_images"): #Create a tensor out of the list of paths filenamesTensor = tf.constant(self.pathList) #Reads a slice of the tensor, for example, if the tensor is of shape [100,2], the slice shape should be [2] (to check if we have problem here) dataset = tf.data.Dataset.from_tensor_slices(filenamesTensor) #for each slice apply the __readImages function dataset = dataset.map(self.__readImages, num_parallel_calls=int( multiprocessing.cpu_count() / 4)) #Authorize repetition of the dataset when one epoch is over. if shuffle: dataset = dataset.shuffle(buffer_size=16, reshuffle_each_iteration=True) #set batch size dataset = dataset.repeat() batched_dataset = dataset.batch(self.batchSize) batched_dataset = batched_dataset.prefetch(buffer_size=4) #Create an iterator to be initialized iterator = batched_dataset.make_initializable_iterator() #Create the node to retrieve next batch paths_batch, inputs_batch, targets_batch, gammadInputBatch = iterator.get_next( ) self.gammaCorrectedInputsBatch = gammadInputBatch reshaped_targets = helpers.target_reshape(targets_batch) #inputRealSize = self.tileSize inputRealSize = self.inputImageSize #Do the random crop, if the crop if fix, crop in the middle if inputRealSize > self.tileSize: if self.fixCrop: xyCropping = (inputRealSize - self.tileSize) // 2 xyCropping = [xyCropping, xyCropping] else: xyCropping = tf.random_uniform([1], 0, inputRealSize - self.tileSize, dtype=tf.int32) inputs_batch = inputs_batch[:, :, xyCropping[0]:xyCropping[0] + self.tileSize, xyCropping[0]:xyCropping[0] + self.tileSize, :] targets_batch = targets_batch[:, :, xyCropping[0]:xyCropping[0] + self.tileSize, xyCropping[0]:xyCropping[0] + self.tileSize, :] #Set shapes inputs_batch.set_shape([None, self.tileSize, self.tileSize, 3]) targets_batch.set_shape( [None, self.nbTargetsToRead, self.tileSize, self.tileSize, 3]) #Populate the object self.stepsPerEpoch = int( math.floor(len(self.pathList) / self.batchSize)) self.inputBatch = inputs_batch self.targetBatch = targets_batch self.iterator = iterator self.pathBatch = paths_batch
def __renderInputs(self, materials, renderingScene, jitterLightPos, jitterViewPos, mixMaterials): fullSizeMixedMaterial = materials if mixMaterials: alpha = tf.random_uniform([1], minval=0.1, maxval=0.9, dtype=tf.float32, name="mixAlpha") materials1 = materials[::2] materials2 = materials[1::2] fullSizeMixedMaterial = helpers.mixMaterials( materials1, materials2, alpha) if self.inputImageSize >= self.tileSize: if self.fixCrop: xyCropping = (self.inputImageSize - self.tileSize) // 2 xyCropping = [xyCropping, xyCropping] else: xyCropping = tf.random_uniform([2], 0, self.inputImageSize - self.tileSize, dtype=tf.int32) cropped_mixedMaterial = fullSizeMixedMaterial[:, :, xyCropping[ 0]:xyCropping[0] + self.tileSize, xyCropping[1]:xyCropping[1] + self.tileSize, :] elif self.inputImageSize < self.tileSize: raise Exception( "Size of the input is inferior to the size of the rendering, please provide higher resolution maps" ) cropped_mixedMaterial.set_shape( [None, self.nbTargetsToRead, self.tileSize, self.tileSize, 3]) mixedMaterial = helpers.adaptRougness(cropped_mixedMaterial) targetstoRender = helpers.target_reshape( mixedMaterial ) #reshape it to be compatible with the rendering algorithm [?, size, size, 12] nbRenderings = 1 rendererInstance = renderer.GGXRenderer(includeDiffuse=True) ## Do renderings of the mixedMaterial targetstoRender = helpers.preprocess( targetstoRender) #Put targets to -1; 1 surfaceArray = helpers.generateSurfaceArray(self.tileSize) inputs = helpers.generateInputRenderings( rendererInstance, targetstoRender, self.batchSize, nbRenderings, surfaceArray, renderingScene, jitterLightPos, jitterViewPos, self.useAmbientLight, useAugmentationInRenderings=self.useAugmentationInRenderings) self.gammaCorrectedInputsBatch = tf.squeeze(inputs, [1]) inputs = tf.pow(inputs, 2.2) # correct gamma if self.logInput: inputs = helpers.logTensor(inputs) inputs = helpers.preprocess(inputs) #Put inputs to -1; 1 targets = helpers.target_deshape(targetstoRender, self.nbTargetsToRead) return targets, inputs
def main(): if a.seed is None: a.seed = random.randint(0, 2**31 - 1) tf.set_random_seed(a.seed) np.random.seed(a.seed) random.seed(a.seed) loadCheckpointOption(a.mode, a.checkpoint) #loads so that I don't mix up options and it generates data corresponding to this training config = tf.ConfigProto() if not os.path.exists(a.output_dir): os.makedirs(a.output_dir) with open(os.path.join(a.output_dir, "options.json"), "w") as f: f.write(json.dumps(vars(a), sort_keys=True, indent=4)) data = dataReader.dataset(a.input_dir, imageFormat = a.imageFormat, trainFolder = a.trainFolder, testFolder = a.testFolder, nbTargetsToRead = a.nbTargets, tileSize=TILE_SIZE, inputImageSize=a.input_size, batchSize=a.batch_size, fixCrop = (a.mode == "test"), mixMaterials = (a.mode == "train" or a.mode == "finetune"), logInput = a.useLog, useAmbientLight = a.useAmbientLight, useAugmentationInRenderings = not a.NoAugmentationInRenderings) # Populate data data.loadPathList(a.inputMode, a.mode, a.mode == "train" or a.mode == "finetune", inputpythonList) if a.feedMethod == "render": if a.mode == "train": data.populateInNetworkFeedGraph(a.renderingScene, a.jitterLightPos, a.jitterViewPos, shuffle = (a.mode == "train" or a.mode == "finetune")) elif a.mode == "finetune": data.populateInNetworkFeedGraphSpatialMix(a.renderingScene, shuffle = False, imageSize = a.input_size) elif a.feedMethod == "files": data.populateFeedGraph(shuffle = (a.mode == "train" or a.mode == "finetune")) if a.mode == "train" or a.mode == "finetune": with tf.name_scope("recurrentTest"): dataTest = dataReader.dataset(a.input_dir, imageFormat = a.imageFormat, testFolder = a.testFolder, nbTargetsToRead = a.nbTargets, tileSize=TILE_SIZE, inputImageSize=a.test_input_size, batchSize=a.batch_size, fixCrop = True, mixMaterials = False, logInput = a.useLog, useAmbientLight = a.useAmbientLight, useAugmentationInRenderings = not a.NoAugmentationInRenderings) dataTest.loadPathList(a.inputMode, "test", False, inputpythonList) if a.testApproach == "render": #dataTest.populateInNetworkFeedGraphSpatialMix(a.renderingScene, shuffle = False, imageSize = TILE_SIZE, useSpatialMix=False) dataTest.populateInNetworkFeedGraph(a.renderingScene, a.jitterLightPos, a.jitterViewPos, shuffle = False) elif a.testApproach == "files": dataTest.populateFeedGraph(False) targetsReshaped = helpers.target_reshape(data.targetBatch) #CreateModel model = mod.Model(data.inputBatch, generatorOutputChannels=9) model.create_model() if a.mode == "train" or a.mode == "finetune": testTargetsReshaped = helpers.target_reshape(dataTest.targetBatch) testmodel = mod.Model(dataTest.inputBatch, generatorOutputChannels=9, reuse_bool=True) testmodel.create_model() display_fetches_test, _ = helpers.display_images_fetches(dataTest.pathBatch, dataTest.inputBatch, dataTest.targetBatch, dataTest.gammaCorrectedInputsBatch, testmodel.output, a.nbTargets, a.logOutputAlbedos) loss = losses.Loss(a.loss, model.output, targetsReshaped, TILE_SIZE, a.batch_size, tf.placeholder(tf.float64, shape=(), name="lr"), a.includeDiffuse, a.nbSpecularRendering, a.nbDiffuseRendering) loss.createLossGraph() loss.createTrainVariablesGraph() #Register Renderings And Loss In Tensorflow display_fetches, converted_images = helpers.display_images_fetches(data.pathBatch, data.inputBatch, data.targetBatch, data.gammaCorrectedInputsBatch, model.output, a.nbTargets, a.logOutputAlbedos) if a.mode == "train": helpers.registerTensorboard(data.pathBatch, converted_images, a.nbTargets, loss.lossValue, a.batch_size, loss.targetsRenderings, loss.outputsRenderings) #Run either training or test with tf.name_scope("parameter_count"): parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables()]) saver = tf.train.Saver(max_to_keep=1) if a.checkpoint is not None: print("reading model from checkpoint : " + a.checkpoint) checkpoint = tf.train.latest_checkpoint(a.checkpoint) partialSaver = helpers.optimistic_saver(checkpoint) #Be careful this will silently not load variables if they are missing from the graph or checkpoint logdir = a.output_dir if a.summary_freq > 0 else None sv = tf.train.Supervisor(logdir=logdir, save_summaries_secs=0, saver=None) with sv.managed_session("", config= config) as sess: sess.run(data.iterator.initializer) print("parameter_count =", sess.run(parameter_count)) if a.checkpoint is not None: print("restoring model from checkpoint : " + a.checkpoint) partialSaver.restore(sess, checkpoint) max_steps = 2**32 if a.max_epochs is not None: max_steps = data.stepsPerEpoch * a.max_epochs if a.max_steps is not None: max_steps = a.max_steps sess.run(data.iterator.initializer) if a.mode == "test": filesets = test(sess, data, max_steps, display_fetches, output_dir = a.output_dir) if a.mode == "train" or a.mode == "finetune": train(sv, sess, data, max_steps, display_fetches, display_fetches_test, dataTest, saver, loss, a.output_dir)
def main(): if a.seed is None: a.seed = random.randint(0, 2**31 - 1) tf.set_random_seed(a.seed) np.random.seed(a.seed) random.seed(a.seed) #Load some options from the checkpoint if we provided one. loadCheckpointOption() #If we feed the network with renderings done in the network for a test run, we save the images before, to be able to compare later with other networks on the same testset. if a.mode == "test" and a.feedMethod == "render": testHelpers.renderTests(a.input_dir, a.testFolder, a.maxImages, tmpFolder, a.imageFormat, CROP_SIZE, a.nbTargets, a.input_size, a.batch_size, a.renderingScene, a.jitterLightPos, a.jitterViewPos, a.inputMode, a.mode, a.output_dir) generateTmpData = True a.nbInputs = a.maxImages a.feedMethod = "files" a.testFolder = tmpFolder a.input_size = CROP_SIZE backupOutputDir = a.output_dir #We run the network once if we a training nbRun = 1 #And as many time as the maximum number of images we want to treat with if testing (to have results with one image, two images, three images etc... to see the improvement) if a.mode == "test": nbRun = a.maxImages #1 a.fixImageNb = True #Now run the network nbRun times. for runID in range(nbRun): maxInputNb = a.maxImages if a.mode == "test": maxInputNb = runID + 1 #a.maxImages a.output_dir = os.path.join(backupOutputDir, str(runID)) tf.reset_default_graph() #Create the output dir if it doesn't exist if not os.path.exists(a.output_dir): os.makedirs(a.output_dir) #Write to the "options" file the different parameters of this run. with open(os.path.join(a.output_dir, "options.json"), "w") as f: f.write(json.dumps(vars(a), sort_keys=True, indent=4)) #Create a dataset object data = dataReader.dataset(a.input_dir, imageType = a.imageFormat, trainFolder = a.trainFolder, testFolder = a.testFolder, inputNumbers = a.nbInputs, maxInputToRead = maxInputNb, nbTargetsToRead = a.nbTargets, cropSize=CROP_SIZE, inputImageSize=a.input_size, batchSize=a.batch_size, fixCrop = (a.mode == "test"), mixMaterials = (a.mode == "train"), fixImageNb = a.fixImageNb, logInput = a.useLog, useAmbientLight = a.useAmbientLight, jitterRenderings = a.jitterRenderings, firstAsGuide = False, useAugmentationInRenderings = not a.NoAugmentationInRenderings, mode = a.mode) # Populate the list of files the dataset will contain data.loadPathList(a.inputMode, a.mode, a.mode == "train") # Depending on wheter we want to render our input data or directly use files, we create the tensorflow data loading system. if a.feedMethod == "render": data.populateInNetworkFeedGraph(a.renderingScene, a.jitterLightPos, a.jitterViewPos, a.mode == "test", shuffle = a.mode == "train") elif a.feedMethod == "files": data.populateFeedGraph(shuffle = a.mode == "train") # Here we reshape the input to have all the images in the first dimension (to treat in parallel) inputReshaped, dyn_batch_size = helpers.input_reshape(data.inputBatch, a.NoMaxPooling, a.maxImages) if a.mode == "train": with tf.name_scope("recurrentTest"): #Initialize different data for tests. dataTest = dataReader.dataset(a.input_dir, imageType = a.imageFormat, testFolder = a.testFolder, inputNumbers = a.nbInputs, maxInputToRead = a.maxImages, nbTargetsToRead = a.nbTargets, cropSize=CROP_SIZE, inputImageSize=a.input_size, batchSize=a.batch_size, fixCrop = True, mixMaterials = False, fixImageNb = a.fixImageNb, logInput = a.useLog, useAmbientLight = a.useAmbientLight, jitterRenderings = a.jitterRenderings, firstAsGuide = a.firstAsGuide, useAugmentationInRenderings = not a.NoAugmentationInRenderings, mode = a.mode) dataTest.loadPathList(a.inputMode, "test", False) if a.feedMethod == "render": dataTest.populateInNetworkFeedGraph(a.renderingScene, a.jitterLightPos, a.jitterViewPos, True, shuffle = False) elif a.feedMethod == "files": dataTest.populateFeedGraph(False) TestinputReshaped, test_dyn_batch_size = helpers.input_reshape(dataTest.inputBatch, a.NoMaxPooling, a.maxImages) #Reshape the targets to [?(Batchsize), 256,256,12] targetsReshaped = helpers.target_reshape(data.targetBatch) #Create the object to contain the network model. model = mod.Model(inputReshaped, dyn_batch_size, last_convolutions_channels = last_convs_chans, generatorOutputChannels=64, useCoordConv = a.useCoordConv, firstAsGuide = a.firstAsGuide, NoMaxPooling = a.NoMaxPooling, pooling_type=a.poolingtype) #Initialize the model. model.create_model() if a.mode == "train": #Initialize the regular test network with different data so that it can run regular test sets. testTargetsReshaped = helpers.target_reshape(dataTest.targetBatch) testmodel = mod.Model(TestinputReshaped, test_dyn_batch_size, last_convolutions_channels = last_convs_chans, generatorOutputChannels=64, reuse_bool=True, useCoordConv = a.useCoordConv, firstAsGuide = a.firstAsGuide, NoMaxPooling = a.NoMaxPooling, pooling_type=a.poolingtype) testmodel.create_model() #Organize the images we want to retrieve from the test network run display_fetches_test, _ = helpers.display_images_fetches(dataTest.pathBatch, dataTest.inputBatch, dataTest.targetBatch, dataTest.gammaCorrectedInputsBatch, testmodel.output, a.nbTargets, a.logOutputAlbedos) # Compute the training network loss. loss = losses.Loss(a.loss, model.output, targetsReshaped, CROP_SIZE, a.batch_size, tf.placeholder(tf.float64, shape=(), name="lr"), a.includeDiffuse) loss.createLossGraph() #Create the training graph part loss.createTrainVariablesGraph() #Organize the images we want to retrieve from the train network run display_fetches, converted_images = helpers.display_images_fetches(data.pathBatch, data.inputBatch, data.targetBatch, data.gammaCorrectedInputsBatch, model.output, a.nbTargets, a.logOutputAlbedos) if a.mode == "train": #Register inputs, targets, renderings and loss in Tensorboard helpers.registerTensorboard(data.pathBatch, converted_images, a.maxImages, a.nbTargets, loss.lossValue, a.batch_size, loss.targetsRenderings, loss.outputsRenderings) #Compute how many paramters the network has with tf.name_scope("parameter_count"): parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables()]) #Initialize a saver saver = tf.train.Saver(max_to_keep=1) if a.checkpoint is not None: print("reading model from checkpoint : " + a.checkpoint) checkpoint = tf.train.latest_checkpoint(a.checkpoint) partialSaver = helpers.optimistic_saver(checkpoint) logdir = a.output_dir if a.summary_freq > 0 else None sv = tf.train.Supervisor(logdir=logdir, save_summaries_secs=0, saver=None) #helpers.print_trainable() with sv.managed_session() as sess: print("parameter_count =", sess.run(parameter_count)) #Loads the checkpoint if a.checkpoint is not None: print("restoring model from checkpoint : " + a.checkpoint) partialSaver.restore(sess, checkpoint) #Evaluate how many steps to run max_steps = 2**32 if a.max_epochs is not None: max_steps = data.stepsPerEpoch * a.max_epochs if a.max_steps is not None: max_steps = a.max_steps #If we want to run a test if a.mode == "test" or a.mode == "eval": filesets = test(sess, data, max_steps, display_fetches, output_dir = a.output_dir) if runID == nbRun - 1 and runID >= 1: #If we are at the last iteration of the test, generate the full html helpers.writeGlobalHTML(backupOutputDir, filesets, a.nbTargets, a.mode, a.maxImages) #If we want to train if a.mode == "train": train(sv, sess, data, max_steps, display_fetches, display_fetches_test, dataTest, saver, loss)
def __renderInputs(self, materials, renderingScene, jitterLightPos, jitterViewPos, mixMaterials, isTest, renderSize): mixedMaterial = materials if mixMaterials: alpha = tf.random_uniform([1], minval=0.1, maxval=0.9, dtype=tf.float32, name="mixAlpha") #print("mat2: " + str(materials2)) materials1 = materials[::2] materials2 = materials[1::2] mixedMaterial = helpers.mixMaterials(materials1, materials2, alpha) mixedMaterial.set_shape( [None, self.nbTargetsToRead, renderSize, renderSize, 3]) mixedMaterial = helpers.adaptRougness(mixedMaterial) #These 3 lines below tries to scale the albedos to get more variety and to randomly flatten the normals to disambiguate the normals and albedos. We did not see strong effect for these. #if not isTest and self.useAugmentationInRenderings: # mixedMaterial = helpers.adaptAlbedos(mixedMaterial, self.batchSize) # mixedMaterial = helpers.adaptNormals(mixedMaterial, self.batchSize) reshaped_targets_batch = helpers.target_reshape( mixedMaterial ) #reshape it to be compatible with the rendering algorithm [?, size, size, 12] nbRenderings = self.maxInputToRead if not self.fixImageNb: #If we don't want a constant number of input images, we randomly select a number of input images between 1 and the maximum number of images defined by the user. nbRenderings = tf.random_uniform([1], 1, self.maxInputToRead + 1, dtype=tf.int32)[0] rendererInstance = renderer.GGXRenderer(includeDiffuse=True) ## Do renderings of the mixedMaterial targetstoRender = reshaped_targets_batch pixelsToAdd = 0 targetstoRender = helpers.preprocess( targetstoRender) #Put targets to -1; 1 surfaceArray = helpers.generateSurfaceArray( renderSize, pixelsToAdd ) #Generate a grid Y,X between -1;1 to act as the pixel support of the rendering (computer the direction vector between each pixel and the light/view) #Do the renderings inputs = helpers.generateInputRenderings( rendererInstance, targetstoRender, self.batchSize, nbRenderings, surfaceArray, renderingScene, jitterLightPos, jitterViewPos, self.useAmbientLight, useAugmentationInRenderings=self.useAugmentationInRenderings) #inputs = [helpers.preprocess(input) for input in inputs] randomTopLeftCrop = tf.zeros([self.batchSize, nbRenderings, 2], dtype=tf.int32) averageCrop = 0.0 #If we want to jitter the renderings around (to try to take into account small non alignment), we should handle the material crop a bit differently #We didn't really manage to get satisfying results with the jittering of renderings. But the code could be useful if this is of interest to Ansys. if self.jitterRenderings: randomTopLeftCrop = tf.random_normal( [self.batchSize, nbRenderings, 2], 0.0, 1.0) #renderSize - self.cropSize, dtype=tf.int32) randomTopLeftCrop = randomTopLeftCrop * tf.exp( tf.random_normal( [self.batchSize], 0.0, 1.0)) #renderSize - self.cropSize, dtype=tf.int32) randomTopLeftCrop = randomTopLeftCrop - tf.reduce_mean( randomTopLeftCrop, axis=1, keep_dims=True) randomTopLeftCrop = tf.round(randomTopLeftCrop) randomTopLeftCrop = tf.cast(randomTopLeftCrop, dtype=tf.int32) averageCrop = tf.cast(self.maxJitteringPixels * 0.5, dtype=tf.int32) randomTopLeftCrop = randomTopLeftCrop + averageCrop randomTopLeftCrop = tf.clip_by_value(randomTopLeftCrop, 0, self.maxJitteringPixels) totalCropSize = self.cropSize inputs, targets = helpers.cutSidesOut(inputs, targetstoRender, randomTopLeftCrop, totalCropSize, self.firstAsGuide, averageCrop) print("inputs shape after" + str(inputs.get_shape())) self.gammaCorrectedInputsBatch = inputs tf.summary.image("GammadInputs", helpers.convert(inputs[0, :]), max_outputs=5) inputs = tf.pow(inputs, 2.2) # correct gamma if self.logInput: inputs = helpers.logTensor(inputs) inputs = helpers.preprocess(inputs) targets = helpers.target_deshape(targets, self.nbTargetsToRead) return targets, inputs
def populateInNetworkFeedGraphSpatialMix(self,renderingScene, shuffle = True, imageSize = 512, useSpatialMix = True): with tf.name_scope("load_images"): #Create a tensor out of the list of paths filenamesTensor = tf.constant(self.pathList) #Reads a slice of the tensor, for example, if the tensor is of shape [100,2], the slice shape should be [2] (to check if we have problem here) dataset = tf.data.Dataset.from_tensor_slices(filenamesTensor) #for each slice apply the __readImages function dataset = dataset.map(self.__readImagesGT, num_parallel_calls=int(multiprocessing.cpu_count() / 4)) #Authorize repetition of the dataset when one epoch is over. #shuffle = True if shuffle: dataset = dataset.shuffle(buffer_size=16, reshuffle_each_iteration=True) #set batch size dataset = dataset.repeat() toPull = self.batchSize if useSpatialMix: toPull = self.batchSize * 2 batched_dataset = dataset.batch(toPull) batched_dataset = batched_dataset.prefetch(buffer_size=4) #Create an iterator to be initialized iterator = batched_dataset.make_initializable_iterator() #Create the node to retrieve next batch paths_batch, targets_batch = iterator.get_next() inputRealSize = imageSize#Should be input image size but changed tmp if useSpatialMix: threshold = 0.5 perlinNoise = tf.expand_dims(tf.expand_dims(helpers.generate_perlin_noise_2d((inputRealSize, inputRealSize), (1,1)), axis = -1), axis = 0) perlinNoise = (perlinNoise + 1.0) * 0.5 perlinNoise = perlinNoise >= threshold perlinNoise = tf.cast(perlinNoise, tf.float32) inverted = 1.0 - perlinNoise materialsMixed1 = targets_batch[::2] * perlinNoise materialsMixed2 = targets_batch[1::2] * inverted fullSizeMixedMaterial = materialsMixed1 + materialsMixed2 targets_batch = fullSizeMixedMaterial paths_batch = paths_batch[::2] targetstoRender = helpers.target_reshape(targets_batch) #reshape it to be compatible with the rendering algorithm [?, size, size, 12] nbRenderings = 1 rendererInstance = renderer.GGXRenderer(includeDiffuse = True) ## Do renderings of the mixedMaterial mixedMaterial = helpers.adaptRougness(targetstoRender) targetstoRender = helpers.preprocess(targetstoRender) #Put targets to -1; 1 surfaceArray = helpers.generateSurfaceArray(inputRealSize) inputs_batch = helpers.generateInputRenderings(rendererInstance, targetstoRender, self.batchSize, nbRenderings, surfaceArray, renderingScene, False, False, self.useAmbientLight, useAugmentationInRenderings = self.useAugmentationInRenderings) targets_batch = helpers.target_deshape(targetstoRender, self.nbTargetsToRead) self.gammaCorrectedInputsBatch = tf.squeeze(inputs_batch, [1]) #tf.summary.image("GammadInputs", helpers.convert(inputs[0, :]), max_outputs=5) inputs_batch = tf.pow(inputs_batch, 2.2) # correct gamma if self.logInput: inputs_batch = helpers.logTensor(inputs_batch) #Do the random crop, if the crop if fix, crop in the middle if inputRealSize > self.tileSize: if self.fixCrop: xyCropping = (inputRealSize - self.tileSize) // 2 xyCropping = [xyCropping, xyCropping] else: xyCropping = tf.random_uniform([1], 0, inputRealSize - self.tileSize, dtype=tf.int32) inputs_batch = inputs_batch[:, :, xyCropping[0] : xyCropping[0] + self.tileSize, xyCropping[0] : xyCropping[0] + self.tileSize, :] targets_batch = targets_batch[:,:, xyCropping[0] : xyCropping[0] + self.tileSize, xyCropping[0] : xyCropping[0] + self.tileSize, :] #Set shapes inputs_batch = tf.squeeze(inputs_batch, [1]) #Before this the input has a useless dimension in 1 as we have only 1 rendering inputs_batch.set_shape([None, self.tileSize, self.tileSize, 3]) targets_batch.set_shape([None, self.nbTargetsToRead, self.tileSize, self.tileSize, 3]) #Populate the object self.stepsPerEpoch = int(math.floor(len(self.pathList) / self.batchSize)) self.inputBatch = inputs_batch self.targetBatch = targets_batch self.iterator = iterator self.pathBatch = paths_batch